Nitrile gloves have become a significant environmental pollutant after the COVID-19 pandemic due to their single-use design. This study examines the capability of P. aeruginosa to use nitrile gloves as its sole carbon energy source. Biodegradation was determined by P. aeruginosa adapting to increasing nitrile glove concentrations at 1%, 3%, and 5% (w/v). The growth kinetics of P. aeruginosa were evaluated, as well as the polymer weight loss. Topographic changes on the glove surfaces were examined using SEM, and FT-IR was used to evaluate the biodegradation products of the nitrile gloves. Following the establishment of a biofilm on the glove surface, the nitrile toxicity was minimized via biodegradation. The result of the average weight loss of nitrile gloves was 2.25%. FT-IR analysis revealed the presence of aldehydes and aliphatic amines associated with biodegradation. SEM showed P. aeruginosa immersed in the EPS matrix, causing the formation of cracks, scales, protrusions, and the presence of semi-spherical particles. We conclude that P. aeruginosa has the capability to use nitrile gloves as its sole carbon source, even up to 5%, through biofilm formation, demonstrating the potential of P. aeruginosa for the degradation of nitrile gloves.

The tropical lake Chapala is an important source of drinking water in western Mexico since it supplies ~65% of the water consumed in the urban city of Guadalajara. To obtain different pollution scenarios, the presence of pollutants in this waterbody was modeled using a coupled hydraulic and transport model. Two water sampling campaigns were modeled. The governing equations were applied using the routines RMA2 and RMA4 in the Surface-Water Modeling System (SMS) software V 8.1. Hydraulic and transport models were calibrated to describe the water level, velocity, and fate of pollutants. The numerical model showed satisfactory results for the simulated data, analyzed against water level, current velocity, and pollutants measurement data through the Relative Percentage Deviation (RPD), except for ~20% of the sites and the 12-month simulation periods. The hydraulic calibrations showed that the dispersion coefficients were higher for nutrients compared to metals, indicating that the nutrients are dispersed throughout the lake and have a stronger impact on the lake’s water quality. The hydraulic model simulations indicated the presence of points in the central-eastern zone, the lowest concentration of ${\mathrm{PO}}_4^{3-}$, which can be attributed to the presence of vortexing. The metal simulations indicated that the dissolved Ni was the best approximation to the measured values. This is the first study on Lake Chapala regarding the modeling fate and transport of pollutants in relation to the prediction of pollution scenarios.

Background/objectives: Amino acids (AAs) play a critical role in diseases such as cystic fibrosis where Pseudomonas aeruginosa PAO1 adapts its metabolism in response to host-derived nutrients. The adaptation influences virulence and complicates antibiotic treatment mainly for the antimicrobial resistance context. D- and L-AAs have been analyzed for their impact on quorum sensing (QS), a mechanism that regulates virulence factors. This research aimed to reconstruct the genome-scale metabolic model (GEM) of P. aeruginosa PAO1 to investigate the metabolic roles of D- and L-AAs in QS-related pathways. Methods: The updated GEM, iJD1249, was reconstructed by using protocols to integrate data from previous models and refined with well-standardized in silico media (LB, M9, and SCFM) to improve flux balance analysis accuracy. The model was used to explore the metabolic impact of D-Met, D-Ala, D-Glu, D-Ser, L-His, L-Glu, L-Arg, and L-Ornithine (L-Orn) at 5 and 50 mM in QS-related pathways, focusing on the effects on bacterial growth and carbon flux distributions. Results: Among the tested AAs, D-Met was the only one that did not enhance the growth rate of P. aeruginosa PAO1, while L-Arg and L-Orn increased fluxes in the L-methionine biosynthesis pathway, influencing the metH gene. These findings suggest a differential metabolic role for D-and L-AAs in QS-related pathways. Conclusions: Our results shed some light on the metabolic impact of AAs on QS-related pathways and their potential role in P. aeruginosa virulence. Future studies should assess D-Met as a potential adjuvant in antimicrobial strategies, optimizing the concentration in combination with antibiotics to maximize its therapeutic effectiveness.

In this study, lignin was chemically modified to promote hydrogel degradation as a source of carbon and nitrogen for a bacterial consortium consisting of P. putida F1, B. cereus and, B. paramycoides. A hydrogel was synthesized using acrylic acid (AA), acrylamide (AM), and 2-acrylamido-2-methyl-1-propanesulfonic acid (AMPS) and cross-linked with the modified lignin. The structural changes and mass loss in the hydrogel, as well as its final composition, were evaluated as functions of the growth of the selected strains in a culture broth with the powdered hydrogel. The average loss was 18.4% wt. The hydrogel was characterized using FTIR spectroscopy, scanning electronic microscopy (SEM), elemental analysis (EA), and thermogravimetric analysis (TGA) before and after bacterial treatment. FTIR showed that the carboxylic groups present in both the lignin and the acrylic acid of the hydrogel decreased during bacterial growth. The bacteria showed a preference for the biomaterial components of the hydrogel. SEM demonstrated superficial morphological changes in the hydrogel. The results reveal that the hydrogel was assimilated by the bacterial consortium while preserving the water retention capacity of the material and that the microorganisms carried out a partial biodegradation of the hydrogel. The results of the EA and TGA confirm that the bacterial consortium not only degraded the biopolymer (lignin), but also used the synthetic hydrogel as a carbon source to degrade its polymeric chains and modified original properties. This modification with lignin as a crosslinker (which is a waste product of the paper industry) is therefore proposed to promote hydrogel degradation.

En el Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), las Unidades de Aprendizaje (UA) relacionadas con la sostenibilidad ambiental, son optativas para las licenciaturas. Por lo anterior, en la Licenciatura de Químico Farmacéutico Biólogo se diseñó y aplicó un reto para promocionar los 17 Objetivos del Desarrollo Sostenible (17 ODS) en los estudiantes de las UA de Seminario de Tutoría. Los instrumentos utilizados fueron: video de sensibilización e invitación al reto, datos del informe ODS 2022 de la Organización de las Naciones Unidas (ONU) y las 170 acciones por el planeta. Se diseñó un formulario de inscripción y otro de envío de evidencias fotográficas, así como una encuesta de la percepción del estudiante al haber realizado el reto. Se obtuvieron 517 (30%) participaciones de 1, 747 (100%) estudiantes activos en el semestre 2023A. El ODS con mayor participación fue ODS 3 “Salud y Bienestar” seguido de ODS 12 “Producción y consumo responsable”. Esta actividad participativa fue motivadora y sensibilizante, primera en su tipo dentro de las Ciencias Exactas e Ingenierías de la Universidad de Guadalajara, arrojando datos que fueron tratados estadísticamente y pueden ser utilizados para propuestas institucionales para incrementar la difusión de los ODS.

CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications.